A big data-based ensemble for fault prediction in electrical secondary distribution network

Abstract

AbstractThe introduction of smart meters, sensors and integrated electronic devices in the electrical secondary distribution network (ESDN) has led to the collection of massive amounts of data. Accurate prediction of faults from this data can help to improve the reliability, safety and operational efficiency of ESDN. Due to its complexity, ESDN big data are hard to process and manage using traditional technologies and tools. The difficulties posed by dataset complexity arise from issues including high dimensionality, imbalance and variability, and one current challenge is to address them simultaneously. Currently, the capability of fault prediction techniques to address this challenge remains limited. New approaches are needed to overcome it. To this purpose, this article presents a big data-based ensemble for fault prediction in ESDN (BDEFP-ESDN) on Apache Spark with gradient-boosted trees, random forest, decision tree and binomial logistic regression base models. BDEFP-ESDN is optimized for the complexity of the ESDN dataset by dimension reduction, bootstrap sampling and hyperparameter optimization approaches in the training process and a weighted voting approach in the prediction process. Our experimental results illustrate the efficiency of BDEFP-ESDN against traditional classifiers like ANN, SVM, RF and XGB, achieving an accuracy of 99.6% for both binary and multiclass classification.